Potential Impacts of Future Extreme Precipitation Changes on Flood Engineering Design Across the Contiguous United States

Abstract

The intensification of extreme precipitation in a warming climate is expected to increase flood risk. In order to support flood resilience efforts, it is important to anticipate and quantify potential changes in design standards under future climate conditions. This study assessed how extreme precipitation is expected to change over the 21st century in relation to current National Oceanic and Atmospheric Administration (NOAA) Atlas 14 design standards over the contiguous United States (CONUS). We used the Community Earth System Model Version 2 large ensemble (CESM2‐LE) simulations from the Coupled Model Intercomparison Project Phase 6 and incorporated future changes into flood engineering design standard with a spatially distributed quantile delta mapping method. Relative changes in extreme daily precipitation were computed for multiple average recurrence intervals (ARIs) up to 100‐year and different planning horizons (2020, 2040, 2060, 2080, and 2100). The results indicated an intensification of extreme precipitation by approximately 10%–40% in northern regions and 20%–80% in southern regions by 2100. The current 100‐year ARI with 24‐hr duration from NOAA Atlas 14 is projected to become the 50‐year ARI in the Northern Great Plains, less than the 25‐year ARI in Southwest areas, and approximately the 25‐year ARI in the other regions by 2100. While a nationwide consensus is still needed, this work presents a possible methodology for incorporating climate uncertainty in engineering design. A comparison across major metropolitan areas also illustrates regional variability in projected changes relative to NOAA Atlas 14, suggesting a need for varied local‐scale responses.

Seasonal variability of future extreme precipitation and associated trends across the Contiguous U.S.

“…A prior study compared the new generation CESM2-LE over CONUS in three different periods, namely 1961-1980, 1981-2000, and 2001-2020 with a reference from 1941 to 1960 (Coelho et al, 2022). The study made use of the available Global Historical Climate Network (GHCN) observing stations data in each CESM2-LE grid box (100km) to construct an empirical cumulative density function (CDF) based on the return period (representing climatological quantiles).…”

“…The present study uses a large ensemble to assess projected seasonal climatology and extremes over the Contiguous United States (CONUS). Future precipitation characteristics are essential for understanding the evolution of the hydrological cycle (Tabari, 2020), adaptive flood engineering design (Madsen et al, 2014;Coelho et al, 2022), climate-resilient water systems (Rahat et al, 2022), and sustainable water resources management (Peters-Lidard et al, 2021).…”

Regionally high risk increase for precipitation extreme events under global warming

“…At the same time, global warming projections of increases in the intensity and frequency of the largest events are also subject to the most uncertainty 21 . An uncertain future also means uncertainty in decision making for, e.g., how we design infrastructure that can withstand increasingly larger and frequent storms 4 , 28 , and how we rethink our cities in general 29 . Given the high impact of these events, an additional motivation of this study is to the search for ways to reduce this uncertainty.…”

Exploring the Future of Rainfall Extremes Over CONUS: The Effects of High Emission Climate Change Trajectories on the Intensity and Frequency of Rare Precipitation Events

“…Concerning IDF curve derivation, most traditional approaches include the essential stationarity assumption (see also Koutsoyiannis et al., 1998; Papalexiou & Koutsoyiannis, 2013; Serinaldi & Kilsby, 2014; Tyralis & Langousis, 2019; Veneziano et al., 2007). In an attempt to practically tackle issues related to the nonstationary nature of the rainfall process, as well as the potential unrepresentativeness of existing precipitation records, numerous studies proposed the update of IDF curves via the use of multiplication factors (commonly known as climate change factors, climate factors of safety, or delta change factors), which are calculated through a direct comparison between estimates obtained from historical data and climate model projections (see e.g., Arnbjerg‐Nielsen, 2012; Coelho et al., 2022; Cook et al., 2017, 2020; Larsen et al., 2009; Lopez‐Cantu et al., 2020; Mailhot et al., 2007; Ragno et al., 2018). However, the use of unrefined and extensive climate model simulations (i.e., longer than 10–20 years) to derive parameter estimates, along with the application of IDF estimation approaches that do not account for limitations introduced by the stationarity assumption, may convey significant epistemic uncertainty into the final results.…”